Ethical Decision-Making Framework Based on Incremental ILP Considering Conflicts

Humans are experiencing the inclusion of artificial agents in their lives, such as unmanned vehicles, service robots, voice assistants, and intelligent medical care. If the artificial agents cannot align with social values or make ethical decisions, they may not meet the expectations of humans. Traditionally, an ethical decision-making framework is constructed by rule-based or statistical approaches. In this paper, we propose an ethical decision-making framework based on incremental ILP (Inductive Logic Programming), which can overcome the brittleness of rule-based approaches and little interpretability of statistical approaches. As the current incremental ILP makes it difficult to solve conflicts, we propose a novel ethical decision-making framework considering conflicts in this paper, which adopts our proposed incremental ILP system. The framework consists of two processes: the learning process and the deduction process. The first process records bottom clauses with their score functions and learns rules guided by the entailment and the score function. The second process obtains an ethical decision based on the rules. In an ethical scenario about chatbots for teenagers’ mental health, we verify that our framework can learn ethical rules and make ethical decisions. Besides, we extract incremental ILP from the framework and compare it with the state-of-the-art ILP systems based on ASP (Answer Set Programming) focusing on conflict resolution. The results of comparisons show that our proposed system can generate better-quality rules than most other systems.